1. Field of the Invention
The present invention relates to electronics, and more particularly, to an automatic intelligent yield improving and process parameter multivariate analysis system and the analysis method thereof by utilizing data mining technology.
2. Description of the Prior Art
In a semiconductor manufacturing process, each set of processes requires a large number of equipment to deal with complicated procedures. Therefore, engineers are concentrated on ensuring the proper operation of equipment, sustaining or improving production yield rate, detecting and verifying problems, and periodically maintaining facilities for production, etc, so as to maintain the company''s operation in optimum conditions.
With the progress of technologies, the complexities of processing are raised and the amount of data is increased to such an extent that tracing and discovering processing problems becomes even more difficult. Although computers and statistical anaysis means are utilized, the prior art data mining method, having no filter mechanism, does not work well in analyzing process parameters because the singularity of processing, the large amount of data, and the complex modules of equipment result in too large an amount of data. Consequently, the characteristic feature of each parameter is not revealed. As a result, the analysis results are fruitless, exhaust manpower to process, and require experts from different areas to analyze.
Since there is no complete set of design of analysis recipes and strict definition for statistical analysis, the analysis results are determined according to humans experience. As a result, the accuracy and the confidence level of the final analysis results are open to question. Furthermore, the human affairs in semiconductor manufacturing change frequently. Engineer''s personal experience is difficult to transfer. The capacity of each engineer is limited, meaning the engineer is unable to look after the operation status of all of the equipment. When the testing results indicate abnormalities, it is thus difficult for engineers, lacking in experience, to judge which point causes the problem to occur. Therefore, a lot of time is consumed to redo related research, and even worse, wrong decisions are made. This will not only increase the cost, but also can not improve the on-line production status in time, making the prior art method unsuitable for semiconductor industry, which upholds high efficiency and high yield rate.
It is therefore very important to provide a complete and effective intelligent decision-making system to assist engineers with trouble-shooting and making right decisions.